Human brains exhibit an approximate bilateral symmetry with respect to the inter-hemispheric (longitudinal) fissure bisecting the brain, known as the MSP. However, human brains are almost never perfectly symmetric (R. J. Davidson, K. Hugdahl, Eds., Brain Asymmetry. Cambridge, Mass.: MIT Press/Bradford Books, 1996). A characteristic of the longitudinal fissure is that it is filled with cerebro-spinal fluid (CSF), which is quite dark in T1-weighted magnetic resonance (MR) images and quite bright in T2-weighted MR images.
MSP detection is often the first step in spatial normalization or anatomical standardization of brain images (J. L. Lancaster, T. G. Glass, B. R. Lankipalli, H. Downs, H. Mayberg, P. T. Fox, ‘A modality-independent approach to spatial normalization of tomographic images of the human brain,’ Human Brain Mapping (1995), 3: 209-223). It is also a useful first step in intrasubject inter/intramodality image registration (I. Kapouleas, A. Alavi, W. M. Alves, R. E. Gur, ‘Registration of three-dimensional MR and PET images of the human brain without markers,’ Radiology (1991), 181:731-739). Determination or extraction of the MSP could provide a powerful tool to detect brain asymmetry due to tumors as well as any mass effects for diagnosis. In addition, extraction of the MSP is a prerequisite for automatically determining the anterior and posterior commissures needed by the Talairach framework. The Talairach framework is a particular methodology that would be recognized by the person skilled in the art.
Due to the great importance of the MSP, its determination and extraction has attracted quite a lot of research work. One paper entitled ‘Robust midsagittal plane extraction from normal and pathological 3D neuroradiology images’ IEEE Transactions on Medical Imaging (2001), 20(3): 175˜192, by Y. Liu, R. T. Collins, W. E. Rothfus presented a cross-correlation algorithm to locate the plane that maximised bilateral symmetry. The algorithm first pre-processed the original 3D images to get edge maps. Then it calculated cross-correlation between axial slices and their reflection around an axis in the edge map to estimate the orientation that maximised the symmetry. From the estimated orientations of all the axial slices the orientation of the MSP was approximated. One of the limitations of the algorithm is its dependence on the edge map, which is quite prone to noise and skull appearances. Furthermore, the algorithm is hardly applicable to clinical environments since it is too time consuming (as long as 7 minutes for a typical volume). The two papers by the same authors entitled ‘Evaluation of a robust midsagittal plane extraction algorithm for coarse, pathology 3D images’ Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI 2000): 81-94, and ‘Automatic bilateral symmetry (midsagittal) plane extraction from pathology 3D neuroradiological images’ Proceeding of SPIE International Symposium on Medical Imaging (1998), vol. 3338: 1529-1539, respectively, have similar drawbacks.
The paper entitled ‘Automatic detection of mid-sagittal plane in 3D brain images’ IEEE Transactions on Medical Imaging (1997), 16(6): 947˜952 by B. A. Ardekani, J. Kershaw, M. Braun, I. Kanno presented a method purely based on symmetry assumption via calculation of cross correlation of grey level distribution around a plane, and took the plane with maximum cross-correlation as the approximated MSP. The algorithm was very sensitive to asymmetry in the 3D images, and could not deal with pathological images. The paper entitled ‘Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis’ IEEE Transactions on Medical Imaging (1997), 16(5): 610˜616 by L. Verard, P. Allain, J. M. Travere, J. C. Baron, D. Bloyet presented a method to extract the MSP via detecting fissure lines in each axial slice. The algorithm failed in those axial slices where the lateral ventricle was present. The algorithm also failed if any of the fissure lines were not straight or too broad, as in the case of excessive CSF.
The paper entitled ‘Hough transform detection of the longitudinal fissure in tomographic head images’ IEEE Transactions on Medical Imaging (1991), 10(1):74˜81 by M. E. Brummer presented a method to extract the MSP via Hough transform (HT) for plane detection. The algorithm needed pre-processing to get appropriate gradient modulus that should be fissure line dominant, which was difficult if not impossible due to noise, skull and skin appearances. The calculation was intensive for HT transformation in all slices. In addition, an incorrect detection of a fissure line in one slice would contribute to the final extraction, because there was no way to correct automatically. If the subjects being imaged were pathological such as containing excessive amount of CSF or having tumors, the algorithm also failed.
Whilst the first method noted above by Liu et. al may deal with both normal and pathological images, none of the suggested algorithms are suitable for clinical application either due to excessively time intensive computing or due to an inability to process the real clinical data with pathology or ubiquitous asymmetry presented in axial slices.
Essentially existing methods either try to extract the MSP via strict symmetry assumption without considering the grey level features of the fissure, or try to extract the MSP via fissure line detection based on low-level image processing techniques without considering the gross symmetry property of the fissure. None of the existing methods meet the current clinical requirements in terms of speed, accuracy, and robustness to clinical data. There is also a need to meet real time requirements for image processing in so far as determining the symmetry in an image.